EAP User Guide

EAP stands for Evolutionary Algorithm in Python, it is dedicated to people who wish to learn how to use evolutionary algorithms and to those who wish to rediscover evolutionary algorithms. The concepts of EAP may be extremely well resumed by some statements of the PEP 20, the Zen of Python by Tim Peters.

• Beautiful is better than ugly.
• Explicit is better than implicit.
• Simple is better than complex.
• Complex is better than complicated.
• Readability counts.
• Special cases aren’t special enough to break the rules.
• Although practicality beats purity.
• In the face of ambiguity, refuse the temptation to guess.
• There should be one– and preferably only one –obvious way to do it.
• Although that way may not be obvious at first unless you’re Canadian.
• If the implementation is hard to explain, it’s a bad idea.
• If the implementation is easy to explain, it may be a good idea.

EAP is the proof that evolutionary algorithms do not need to be neither complex or complicated.

Evolutionary Content

• 1. Evolutionary Algorithm Bases
  • 1.1. Container Types
  • 1.2. Fitness
  • 1.3. Creator
  • 1.4. Population, Individual and Other Structures
• 2. Evolutionary Toolbox
  • 2.1. Operators
• 3. Algorithms
• 4. Genetic Programming
• 5. Statistics
  • 5.1. Retrieving the information
  • 5.2. Observing something else
• 6. Hall of Fame
• 7. Milestone
• 8. History
• 9. Covariance Matrix Adaptation Evolution Strategy

Tutorials

• 1. First Steps of Evolution
  • 1.1. A First Individual
  • 1.2. Mutation
  • 1.3. Crossover
  • 1.4. Evaluation
  • 1.5. Selection
  • 1.6. The Toolbox
  • 1.7. The Algorithms
• 2. One Max Genetic Algorithm
  • 2.1. Setting Things Up
  • 2.2. The Evaluation Function
  • 2.3. The Genetic Operators
  • 2.4. Evolving the Population
• 3. Short One Max Genetic Algorithm
• 4. Individual Inheriting from Set
• 5. Using Multiple Processors
  • 5.1. Multiprocessing Module

Indices and tables

• Index
• Module Index
• Search Page